A generative model for image segmentation based on label fusion

IEEE Transactions on Medical Imaging

Volumn 29, Issue 10, 2010, Pages 1714-1729

  Sabuncu, Mert R ^a,b   Yeo, B T Thomas ^a,b   Van Leemput, Koen ^a   Fischl, Bruce ^a,b   Golland, Polina ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

Image parcellation; Image registration; Image segmentation

Indexed keywords

ALZHEIMER'S DISEASE; ANATOMICAL VARIABILITY; AUTOMATIC SEGMENTATIONS; BRAIN MRI; BRAIN SEGMENTATION; CEREBRAL CORTEX; FUSION ALGORITHMS; FUSION METHODS; GENERATIVE MODEL; IMAGE PARCELLATION; INFERENCE ALGORITHM; MEDICAL IMAGES; NON-PARAMETRIC; PROBABILISTIC FRAMEWORK; PROBABILISTIC MODELS; SEGMENTATION ALGORITHMS; SEGMENTATION TOOL; TEST IMAGES; TRAINING IMAGE; TRAINING SETS; VOLUME CHANGE; WHITE MATTER;

ALGORITHMS; BRAIN; EXPERIMENTS; IMAGE REGISTRATION; INFERENCE ENGINES; MEDICAL IMAGING;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUDIOVISUAL EQUIPMENT; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BRAIN; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, ANATOMIC; MODELS, NEUROLOGICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 77957561551     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2010.2050897     Document Type: Article

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